Quantitative DCE Dynamics on Transformed MR Imaging Discriminates Clinically Significant Prostate Cancer - Zhouping Wei Ph.D; Malinda Iluppangama M.S., Ph.D; Jin Qi M.D.; Jung W. Choi M.D., Ph.D; Alice Yu M.D.; Kenneth Gage M.D.; Vaibhav Chumbalkar M.D.; Jasreman Dhilon M.D.; K. C. Balaji M.D.; Satish Venkataperumal M.D.; David J. Hernandez M.D.; Jong Park Ph.D; et al. (Cancer Control)
Dynamic contrast enhancement (DCE) imaging is a valuable sequence of multiparametric magnetic resonance imaging (mpMRI). A DCE sequence enhances the vasculature and complements T2-weighted (T2W) and Diffusion-weighted imaging (DWI), allowing early detection of prostate cancer. However, DCE assessment has remained primarily qualitative. The study proposes quantifying DCE characteristics (T1W sequences) using six time-dependent metrics computed on feature transformations (306 radiomic features) of abnormal image regions observed over time. We applied our methodology to prostate cancer patients with the DCE MRI images (n = 25) who underwent prostatectomy with confirmed pathological assessment of the disease using Gleason Score. Regions of abnormality were assessed on the T2W MRI, guided using the whole mount pathology. Preliminary analysis finds over six temporal DCE imaging features obtained on different transformations on the imaging regions showed significant differences compared to the indolent counterpart ( P ≤ 0.05, q ≤ 0.01). We find classifier models using logistic regression formed on DCE features after feature-based transformation (Centre of Mass) had an AUC of 0.89-0.94. While using mean feature-based transformation, the AUC was in the range of 0.71-0.76, estimated using the 0.632 bootstrap cross-validation method and after applying sample balancing using the synthetic minority oversampling technique (SMOTE). Our study finds, radiomic transformation of DCE images (T1 sequences) provides better signal standardization. Their temporal characteristics allow improved discrimination of aggressive disease.